We present a method and web server for predicting DNA structural features in a high-throughput (HT) manner for massive sequence data. This approach provides the framework for the integration of DNA sequence and shape analyses in genome-wide studies. The HT methodology uses a sliding-window approach to mine DNA structural information obtained from Monte Carlo simulations. It requires only nucleotide sequence as input and instantly predicts multiple structural features of DNA (minor groove width, roll, propeller twist and helix twist). The results of rigorous validations of the HT predictions based on DNA structures solved by X-ray crystallography and NMR spectroscopy, hydroxyl radical cleavage data, statistical analysis and cross-validation, and molecular dynamics simulations provide strong confidence in this approach. The DNAshape web server is freely available at http://rohslab.cmb.usc.edu/DNAshape/.

The width of the DNA minor groove varies with sequence and can be a major determinant of DNA shape recognition by proteins. For example, the minor groove within the center of the Fis–DNA complex narrows to about half the mean minor groove width of canonical B-form DNA to fit onto the protein surface. G/C base pairs within this segment, which is not contacted by the Fis protein, reduce binding affinities up to 2000-fold over A/T-rich sequences. We show here through multiple X-ray structures and binding properties of Fis–DNA complexes containing base analogs that the 2-amino group on guanine is the primary molecular determinant controlling minor groove widths. Molecular dynamics simulations of free-DNA targets with canonical and modified bases further demonstrate that sequence-dependent narrowing of minor groove widths is modulated almost entirely by the presence of purine 2-amino groups. We also provide evidence that protein-mediated phosphate neutralization facilitates minor groove compression and is particularly important for binding to non-optimally shaped DNA duplexes.

Proton transfer in cytochrome c oxidase (CcO) from the cellular inside to the binuclear redox centre as well as proton pumping through the membrane takes place through proton entrance via two distinct pathways, the D- and K-channel. Both channels show a dependence of their hydration level on the protonation states of their key residues, K362 for the K-channel, and E286 or D132 for the D-channel. In the oxidative half of CcO's catalytic cycle the D-channel is the proton-conducting path. For this channel, an interplay of protonation state of the D-channel residues with the water and hydrogen-bond dynamics has been observed in molecular dynamics simulations of the CcO protein, embedded in a lipid bi-layer, modelled in different protonation states. Protonation of residue E286 at the end of the D-channel results in a hydrogen-bonded network pointing from E286 to N139, that is against proton transport, and favouring N139 conformations which correspond to a closed asparagine gate (formed by residues N121 and N139). Consequently, the hydration level is lower than with unprotonated E286. In those models, the Asn gate is predominantly open, allowing water molecules to pass and thus increase the hydration level. The hydrogen-bonded network in these states exhibits longer life times of the Asn residues with water than other models and shows the D-channel to be traversable from the entrance, D132, to exit, E286. The D-channel can thus be regarded as auto-regulated with respect to proton transport, allowing proton passage only when required, that is the proton is located at the lower part of the D-channel (D132 to Asn gate) and not at the exit (E286).

Functionalized nanoparticle networks offer a model system for the study of charge transport in low-dimensional systems as well as a potential platform to implement and test electronic functionalities. The electrical response of a nanoparticle network is expected to sensitively depend on the molecular interconnects, i.e., on the linker chemistry. If these linkers have complex charge transport properties, then phenomenological models addressing the large-scale properties of the network need to be complemented with microscopic calculations of the network building blocks. In this study we focus on the interplay between conformational fluctuations and electronic π-stacking in single-molecule junctions and use the obtained microscopic information on their electrical transport properties to parametrize transition rates describing charge diffusion in mesoscopic nanoparticle networks. Our results point out the strong influence of mechanical degrees of freedom on the electronic transport signatures of the studied molecules. This is then reflected in the varying charge diffusion at the network level. The modeling studies are complemented with first charge transport measurements at the single-molecule level of π-stacked molecular dimers using state-of-the-art mechanically controllable break junction techniques in a liquid environment.